Image quality for follow up reading and longitudinal change assessment is an important task in medical imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). The task of recognizing changes in medical images is a technical problem due to the challenge of distinguishing pathological from normal changes in the medical images. For example, for a follow up scan of a lung or other organ of a patient, normal changes such as respiration or normal anatomical differences may mask pathological changes such as cancerous nodule growth or shrinkage.
Detecting pathological changes in CT images or MRI images acquired at two or more time points is difficult due to the large amount of normal changes that may occur. Manual detection of normal vs pathological changes may be difficult or error prone. Computer-assisted image registration may be used to provide an improvement and increase in objectivity of the results. Image registration may be categorized into two groups: rigid and non-rigid. Non-rigid image registration is also known as deformable image registration (DIR). In rigid image registration (RIR), all pixels move and/or rotate uniformly so that every pixel-to-pixel relationship remains the same before and after transformation. In DIR, however, the pixel-to-pixel relationships change, to model a non-linear deformation.
RIR is very effective in cases when no anatomic change nor deformations are expected. However, some patients may experience anatomical structure changes due to weight loss, tumor shrinkage, and/or physiological organ shape variation. The changes may not be handled well by RIR. In comparison to RIR, DIR has a significantly greater flexibility. DIR can manage local distortion between two image sets (e.g. anatomical structure changes). For DIR, mathematical modeling uses known information to find a statistic of motion or deformation in considered organs. Segmentation uses the information to map a contour from a reference image to updated images. DIR may detect and use anatomical landmarks to register sets of images. The methods, however do not distinguish between normal anatomical changes and pathological changes. In an example, a growth of a tumor may be suppressed in a follow up image if the DIR is too strong. Current computer-assisted tools such as DIR may be inaccurate due to normal anatomical changes represented in the images and an inability to distinguish abnormal changes are normal changes and as such, provide inconsistent and confusing image registration.